\section{Introduction}
Advances in processor technology enabled financial traders to employ
various degrees of mathematically complex algorithms in real time 
trading as well as backtesting. Parallel programming has gained 
in popularity as multicore machines began to dominate industry.  

Top end multi-core CPU machines for the latest processors such as Intel Xeon
allow the user to control up to 16 threads using various programming 
languages such as OPENMP.  By contrast, a single GPGPU machine allows the user
to control up to 65k threads.  As a result of the computational 
capability of GPGPU machines and the development of the CUDA C language,
programmers are beginning to move the computationally extensive and parallel 
regions of their code to the GPGPUs.  

This thesis will present two computationally expensive algorithms
that are migrated from a CPU environment to a GPU environment and analyze the 
challenges and results of performing calculations in the GPU.  

Chapter 2 will present a review of the Log-Laplace distribution (Kozuboski and Podgorski, 2003). Give an overview of the maximum likelihood 
estimators for this distribution, and present an algorthm for fitting data to 
this algorithm.

Chapter 3 will present a review of the Assymetric Exponential Power 
distribution (Ayebo and KozuBoski, 2003).  The maximum likelihood estimators 
for certain parameters and an algorithm for finding estimaors for all parameterswill be presented.

Chapter 4 will begin with a review of CUDA C, then discuss the challenges and techniques  of coding the algorithms in C++ and CUDA C. Various acceleration techniques in CUDA will be presented.

Chapter 5 will present the results of CUDA C against the results of C++, from 
a performance perspective.  

Chapter 6 will discuss a guidline for similar work in the future and a
guide to identifying algorithms that are good candidates for CUDA C 
acceleration.       
